{"id":4112384,"date":"2026-01-02T05:49:08","date_gmt":"2026-01-02T10:49:08","guid":{"rendered":"https:\/\/www.computerworld.com\/article\/4112384\/deepseeks-new-method-can-train-ai-more-efficiently-and-cheaply.html"},"modified":"2026-01-06T14:07:40","modified_gmt":"2026-01-06T19:07:40","slug":"deepseeks-new-method-can-train-ai-more-efficiently-and-cheaply","status":"publish","type":"post","link":"https:\/\/www.computerworld.com\/article\/4112384\/deepseeks-new-method-can-train-ai-more-efficiently-and-cheaply.html","title":{"rendered":"Deepseek says new method can train AI more efficiently and cheaply"},"content":{"rendered":"
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Chinese AI company Deepseek has unveiled a new training method, Manifold-Constrained Hyper-Connections (mHC), which will make it possible to train large language models more efficiently and at lower cost, reports the South China Morning Post<\/a>.<\/p>\n\n\n\n

The method is a further development of so-called Hyper-Connections, which was originally developed by Bytedance in 2024. That technology, in turn, builds on the classic ResNet architecture from Microsoft Research Asia.<\/p>\n\n\n\n

Deepseek says mHC provides more stable and scalable training without increasing computational costs, thanks to specific optimizations at the infrastructure level. The researchers have tested the technology on models with up to 27 billion parameters with positive results.<\/p>\n\n\n\n

According to experts cited by the South China Morning Post, the new method could be a foretaste of the next big model release from Deepseek. The company launched its high-profile R1 model on the occasion of Chinese New Year 2025.<\/p>\n\n\n\n

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